Load Data

dataset <- read.delim("raw_data/FigureS3_bleo.txt", stringsAsFactors = FALSE)

dataset$genotype <- gsub(" ", "", dataset$genotype )
dataset$genotype <- factor(dataset$genotype)
dataset$Experiment <- factor(rep(paste0("exp", 1:(length(dataset$genotype)/length(levels(dataset$genotype)))), each=length(unique(dataset$genotype))))

dataset$siRNA <-  factor(gsub(".*[T,O]\\+","",dataset$genotype))
dataset$genotype <-  factor(gsub("\\+.*","",dataset$genotype))

dataset$UID <- factor(paste(dataset$Experiment, dataset$genotype, dataset$siRNA))
dataset$GSID <- factor(paste(dataset$genotype, dataset$siRNA))

# wide format
kable(dataset, row.names = F)
genotype NT bleomycin_5uM bleomycin_10uM bleomycin_50uM Experiment siRNA UID GSID
WT 457 286 170 51 exp1 siCtrl exp1 WT siCtrl WT siCtrl
WT 199 58 55 14 exp1 siPARP1 exp1 WT siPARP1 WT siPARP1
ALC1KO 516 85 57 27 exp1 siCtrl exp1 ALC1KO siCtrl ALC1KO siCtrl
ALC1KO 145 41 24 4 exp1 siPARP1 exp1 ALC1KO siPARP1 ALC1KO siPARP1
WT 1745 1146 656 222 exp2 siCtrl exp2 WT siCtrl WT siCtrl
WT 1356 368 298 33 exp2 siPARP1 exp2 WT siPARP1 WT siPARP1
ALC1KO 2384 460 361 65 exp2 siCtrl exp2 ALC1KO siCtrl ALC1KO siCtrl
ALC1KO 2208 864 551 45 exp2 siPARP1 exp2 ALC1KO siPARP1 ALC1KO siPARP1
WT 1912 1436 722 342 exp3 siCtrl exp3 WT siCtrl WT siCtrl
WT 1896 422 276 45 exp3 siPARP1 exp3 WT siPARP1 WT siPARP1
ALC1KO 3403 1988 389 58 exp3 siCtrl exp3 ALC1KO siCtrl ALC1KO siCtrl
ALC1KO 1808 551 268 77 exp3 siPARP1 exp3 ALC1KO siPARP1 ALC1KO siPARP1
library(reshape2)
# reshape to long format
dataset <- melt(dataset, variable.name = "Treatment", value.name = "Counts")

dataset$genotype <- relevel(dataset$genotype, ref = "WT")
dataset$siRNA <- relevel(dataset$siRNA, ref = "siCtrl")
dataset$UID <- relevel(dataset$UID, ref = "exp1 WT siCtrl")

dataset$Bleomycin <- gsub("NT","1",dataset$Treatment)
dataset$Bleomycin <- gsub("bleomycin_|uM","",dataset$Bleomycin)
dataset$Bleomycin <- log10(as.integer(dataset$Bleomycin))




dataset$Offset <- NA
for(uid in levels(dataset$UID)){
        dataset$Offset[dataset$UID == uid] <- mean(dataset$Counts[dataset$UID == uid])
}

dataset$NormCounts <- dataset$Counts / dataset$Offset



dataset$Offset2 <- NA
for(gsid in levels(dataset$GSID)){
        dataset$Offset2[dataset$GSID == gsid] <- mean(dataset$NormCounts[dataset$GSID == gsid & dataset$Bleomycin == 0])
}

dataset$NormCounts2 <- dataset$NormCounts / dataset$Offset2



# long format
kable(dataset, row.names = F)
genotype Experiment siRNA UID GSID Treatment Counts Bleomycin Offset NormCounts Offset2 NormCounts2
WT exp1 siCtrl exp1 WT siCtrl WT siCtrl NT 457 0.00000 241.00 1.8962656 1.827223 1.0377853
WT exp1 siPARP1 exp1 WT siPARP1 WT siPARP1 NT 199 0.00000 81.50 2.4417178 2.651650 0.9208296
ALC1KO exp1 siCtrl exp1 ALC1KO siCtrl ALC1KO siCtrl NT 516 0.00000 171.25 3.0131387 2.753656 1.0942322
ALC1KO exp1 siPARP1 exp1 ALC1KO siPARP1 ALC1KO siPARP1 NT 145 0.00000 53.50 2.7102804 2.597563 1.0433936
WT exp2 siCtrl exp2 WT siCtrl WT siCtrl NT 1745 0.00000 942.25 1.8519501 1.827223 1.0135325
WT exp2 siPARP1 exp2 WT siPARP1 WT siPARP1 NT 1356 0.00000 513.75 2.6394161 2.651650 0.9953863
ALC1KO exp2 siCtrl exp2 ALC1KO siCtrl ALC1KO siCtrl NT 2384 0.00000 817.50 2.9162080 2.753656 1.0590314
ALC1KO exp2 siPARP1 exp2 ALC1KO siPARP1 ALC1KO siPARP1 NT 2208 0.00000 917.00 2.4078517 2.597563 0.9269657
WT exp3 siCtrl exp3 WT siCtrl WT siCtrl NT 1912 0.00000 1103.00 1.7334542 1.827223 0.9486822
WT exp3 siPARP1 exp3 WT siPARP1 WT siPARP1 NT 1896 0.00000 659.75 2.8738158 2.651650 1.0837840
ALC1KO exp3 siCtrl exp3 ALC1KO siCtrl ALC1KO siCtrl NT 3403 0.00000 1459.50 2.3316204 2.753656 0.8467364
ALC1KO exp3 siPARP1 exp3 ALC1KO siPARP1 ALC1KO siPARP1 NT 1808 0.00000 676.00 2.6745562 2.597563 1.0296407
WT exp1 siCtrl exp1 WT siCtrl WT siCtrl bleomycin_5uM 286 0.69897 241.00 1.1867220 1.827223 0.6494674
WT exp1 siPARP1 exp1 WT siPARP1 WT siPARP1 bleomycin_5uM 58 0.69897 81.50 0.7116564 2.651650 0.2683825
ALC1KO exp1 siCtrl exp1 ALC1KO siCtrl ALC1KO siCtrl bleomycin_5uM 85 0.69897 171.25 0.4963504 2.753656 0.1802514
ALC1KO exp1 siPARP1 exp1 ALC1KO siPARP1 ALC1KO siPARP1 bleomycin_5uM 41 0.69897 53.50 0.7663551 2.597563 0.2950285
WT exp2 siCtrl exp2 WT siCtrl WT siCtrl bleomycin_5uM 1146 0.69897 942.25 1.2162377 1.827223 0.6656207
WT exp2 siPARP1 exp2 WT siPARP1 WT siPARP1 bleomycin_5uM 368 0.69897 513.75 0.7163017 2.651650 0.2701343
ALC1KO exp2 siCtrl exp2 ALC1KO siCtrl ALC1KO siCtrl bleomycin_5uM 460 0.69897 817.50 0.5626911 2.753656 0.2043433
ALC1KO exp2 siPARP1 exp2 ALC1KO siPARP1 ALC1KO siPARP1 bleomycin_5uM 864 0.69897 917.00 0.9422028 2.597563 0.3627257
WT exp3 siCtrl exp3 WT siCtrl WT siCtrl bleomycin_5uM 1436 0.69897 1103.00 1.3019039 1.827223 0.7125040
WT exp3 siPARP1 exp3 WT siPARP1 WT siPARP1 bleomycin_5uM 422 0.69897 659.75 0.6396362 2.651650 0.2412220
ALC1KO exp3 siCtrl exp3 ALC1KO siCtrl ALC1KO siCtrl bleomycin_5uM 1988 0.69897 1459.50 1.3621103 2.753656 0.4946553
ALC1KO exp3 siPARP1 exp3 ALC1KO siPARP1 ALC1KO siPARP1 bleomycin_5uM 551 0.69897 676.00 0.8150888 2.597563 0.3137898
WT exp1 siCtrl exp1 WT siCtrl WT siCtrl bleomycin_10uM 170 1.00000 241.00 0.7053942 1.827223 0.3860471
WT exp1 siPARP1 exp1 WT siPARP1 WT siPARP1 bleomycin_10uM 55 1.00000 81.50 0.6748466 2.651650 0.2545007
ALC1KO exp1 siCtrl exp1 ALC1KO siCtrl ALC1KO siCtrl bleomycin_10uM 57 1.00000 171.25 0.3328467 2.753656 0.1208745
ALC1KO exp1 siPARP1 exp1 ALC1KO siPARP1 ALC1KO siPARP1 bleomycin_10uM 24 1.00000 53.50 0.4485981 2.597563 0.1726996
WT exp2 siCtrl exp2 WT siCtrl WT siCtrl bleomycin_10uM 656 1.00000 942.25 0.6962059 1.827223 0.3810185
WT exp2 siPARP1 exp2 WT siPARP1 WT siPARP1 bleomycin_10uM 298 1.00000 513.75 0.5800487 2.651650 0.2187501
ALC1KO exp2 siCtrl exp2 ALC1KO siCtrl ALC1KO siCtrl bleomycin_10uM 361 1.00000 817.50 0.4415902 2.753656 0.1603651
ALC1KO exp2 siPARP1 exp2 ALC1KO siPARP1 ALC1KO siPARP1 bleomycin_10uM 551 1.00000 917.00 0.6008724 2.597563 0.2313216
WT exp3 siCtrl exp3 WT siCtrl WT siCtrl bleomycin_10uM 722 1.00000 1103.00 0.6545784 1.827223 0.3582367
WT exp3 siPARP1 exp3 WT siPARP1 WT siPARP1 bleomycin_10uM 276 1.00000 659.75 0.4183403 2.651650 0.1577660
ALC1KO exp3 siCtrl exp3 ALC1KO siCtrl ALC1KO siCtrl bleomycin_10uM 389 1.00000 1459.50 0.2665296 2.753656 0.0967912
ALC1KO exp3 siPARP1 exp3 ALC1KO siPARP1 ALC1KO siPARP1 bleomycin_10uM 268 1.00000 676.00 0.3964497 2.597563 0.1526237
WT exp1 siCtrl exp1 WT siCtrl WT siCtrl bleomycin_50uM 51 1.69897 241.00 0.2116183 1.827223 0.1158141
WT exp1 siPARP1 exp1 WT siPARP1 WT siPARP1 bleomycin_50uM 14 1.69897 81.50 0.1717791 2.651650 0.0647820
ALC1KO exp1 siCtrl exp1 ALC1KO siCtrl ALC1KO siCtrl bleomycin_50uM 27 1.69897 171.25 0.1576642 2.753656 0.0572563
ALC1KO exp1 siPARP1 exp1 ALC1KO siPARP1 ALC1KO siPARP1 bleomycin_50uM 4 1.69897 53.50 0.0747664 2.597563 0.0287833
WT exp2 siCtrl exp2 WT siCtrl WT siCtrl bleomycin_50uM 222 1.69897 942.25 0.2356063 1.827223 0.1289422
WT exp2 siPARP1 exp2 WT siPARP1 WT siPARP1 bleomycin_50uM 33 1.69897 513.75 0.0642336 2.651650 0.0242240
ALC1KO exp2 siCtrl exp2 ALC1KO siCtrl ALC1KO siCtrl bleomycin_50uM 65 1.69897 817.50 0.0795107 2.753656 0.0288746
ALC1KO exp2 siPARP1 exp2 ALC1KO siPARP1 ALC1KO siPARP1 bleomycin_50uM 45 1.69897 917.00 0.0490731 2.597563 0.0188920
WT exp3 siCtrl exp3 WT siCtrl WT siCtrl bleomycin_50uM 342 1.69897 1103.00 0.3100635 1.827223 0.1696911
WT exp3 siPARP1 exp3 WT siPARP1 WT siPARP1 bleomycin_50uM 45 1.69897 659.75 0.0682077 2.651650 0.0257227
ALC1KO exp3 siCtrl exp3 ALC1KO siCtrl ALC1KO siCtrl bleomycin_50uM 58 1.69897 1459.50 0.0397396 2.753656 0.0144316
ALC1KO exp3 siPARP1 exp3 ALC1KO siPARP1 ALC1KO siPARP1 bleomycin_50uM 77 1.69897 676.00 0.1139053 2.597563 0.0438508

Plot Data

library(ggplot2)

# raw data
ggplot(dataset, aes(x=Bleomycin, y=Counts)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_smooth(method=lm, formula = y ~ poly(x,2), se=FALSE, aes(colour=siRNA)) +
        geom_point(aes(colour=siRNA, shape=Experiment), size=2) +        
        facet_grid(. ~ genotype) +
        xlab(label = "Bleomycin (log10 µM)") +
        scale_shape_manual(values=15:20) +
        scale_color_manual(values=c("#000000","#FF0000"))

# NormCounts Linear
ggplot(dataset, aes(x=Bleomycin, y=NormCounts, color=siRNA)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_point(aes(colour=siRNA), size=2) +        
        geom_smooth(method=lm, formula = y ~ x, se=FALSE) +
        facet_grid(. ~ genotype) +
        xlab(label = "Bleomycin (log10 µM)") +
        scale_color_manual(values=c("#000000","#FF0000"))

# NormCounts2 Linear
ggplot(dataset, aes(x=Bleomycin, y=NormCounts2, color=siRNA)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_point(aes(colour=siRNA), size=2) +        
        geom_smooth(method=lm, formula = y ~ x, se=FALSE) +
        facet_grid(. ~ genotype) +
        xlab(label = "Bleomycin (log10 µM)") +
        scale_color_manual(values=c("#000000","#FF0000"))

# NormCounts Quadratic
ggplot(dataset, aes(x=Bleomycin, y=NormCounts, color=siRNA)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_point(aes(colour=siRNA), size=2) +        
        geom_smooth(method=lm, formula = y ~ poly(x,2), se=FALSE) +
        facet_grid(. ~ genotype) +
        xlab(label = "Bleomycin (log10 µM)")+
        scale_color_manual(values=c("#000000","#FF0000"))

# NormCounts2 Quadratic
ggplot(dataset, aes(x=Bleomycin, y=NormCounts2, color=siRNA)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_point(aes(colour=siRNA), size=2) +        
        geom_smooth(method=lm, formula = y ~ poly(x,2), se=FALSE) +
        facet_grid(. ~ genotype) +
        xlab(label = "Bleomycin (log10 µM)") +
        scale_color_manual(values=c("#000000","#FF0000"))

# NormCounts Cubic
ggplot(dataset, aes(x=Bleomycin, y=NormCounts, color=siRNA)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_point(aes(colour=siRNA), size=2) +        
        geom_smooth(method=lm, formula = y ~ poly(x,3), se=FALSE) +
        facet_grid(. ~ genotype) +
        xlab(label = "Bleomycin (log10 µM)")+
        scale_color_manual(values=c("#000000","#FF0000"))

# NormCounts2 Cubic
ggplot(dataset, aes(x=Bleomycin, y=NormCounts2, color=siRNA)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_point(aes(colour=siRNA), size=2) +        
        geom_smooth(method=lm, formula = y ~ poly(x,3), se=FALSE) +
        facet_grid(. ~ genotype) +
        xlab(label = "Bleomycin (log10 µM)") +
        scale_color_manual(values=c("#000000","#FF0000"))

library(Cairo)


cairo_pdf("FigureS3_bleo.pdf", width = 5, height = 4, family = "Arial")

ggplot(dataset, aes(x=Bleomycin, y=NormCounts2)) + 
        theme_bw() +
        theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank(), 
              axis.line = element_line(colour = "black"), text = element_text(size=14),
              panel.border = element_blank(), panel.background = element_blank()) +
        geom_point(aes(colour = siRNA, shape = genotype), size=1.75) +
        geom_smooth(method=lm, formula = y ~ poly(x,2), se=TRUE, 
                    aes(group = GSID,colour = siRNA, linetype = genotype), fill='#DDDDDD', size=0.5) +
        #facet_grid(. ~ genotype) +
        xlab(label = "Bleomycin (log10 µM)") +
        ylab(label = "Normalized Counts") +
        scale_color_manual(values=c("#000000","#FF0000")) +
        guides(linetype = guide_legend(override.aes= list(color = "#555555"))) 

dev.off()
## quartz_off_screen 
##                 2

Models

library(MASS)
library(DHARMa)
library(lme4)
library(lmerTest)
library(bbmle)

Linear formula

fit1 <- lm(Counts ~ Experiment + Bleomycin*siRNA*genotype, data = dataset)
print(summary(fit1))
## 
## Call:
## lm(formula = Counts ~ Experiment + Bleomycin * siRNA * genotype, 
##     data = dataset)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -844.27 -280.80  -46.55  227.69 1204.98 
## 
## Coefficients:
##                                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                             865.77     253.57   3.414 0.001533 ** 
## Experimentexp2                          660.81     167.46   3.946 0.000331 ***
## Experimentexp3                          837.75     167.46   5.003 1.32e-05 ***
## Bleomycin                              -710.09     224.14  -3.168 0.003025 ** 
## siRNAsiPARP1                           -397.90     331.51  -1.200 0.237463    
## genotypeALC1KO                          494.49     331.51   1.492 0.144044    
## Bleomycin:siRNAsiPARP1                   63.74     316.98   0.201 0.841702    
## Bleomycin:genotypeALC1KO               -518.54     316.98  -1.636 0.110119    
## siRNAsiPARP1:genotypeALC1KO            -243.48     468.82  -0.519 0.606537    
## Bleomycin:siRNAsiPARP1:genotypeALC1KO   376.67     448.27   0.840 0.406014    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 473.6 on 38 degrees of freedom
## Multiple R-squared:  0.7123, Adjusted R-squared:  0.6442 
## F-statistic: 10.46 on 9 and 38 DF,  p-value: 6.495e-08
cat("AIC: ", AIC(fit1))
## AIC:  738.4095
simres <- simulateResiduals(fittedModel = fit1)
plot(simres)

fit2 <- lm(NormCounts ~ Bleomycin*siRNA*genotype, data = dataset)
print(summary(fit2))
## 
## Call:
## lm(formula = NormCounts ~ Bleomycin * siRNA * genotype, data = dataset)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.7392 -0.3245  0.0422  0.3185  0.6835 
## 
## Coefficients:
##                                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                             1.8106     0.2000   9.055 3.12e-11 ***
## Bleomycin                              -0.9543     0.1912  -4.991 1.22e-05 ***
## siRNAsiPARP1                            0.4370     0.2828   1.545   0.1302    
## genotypeALC1KO                          0.5191     0.2828   1.836   0.0739 .  
## Bleomycin:siRNAsiPARP1                 -0.5144     0.2704  -1.902   0.0643 .  
## Bleomycin:genotypeALC1KO               -0.6110     0.2704  -2.260   0.0294 *  
## siRNAsiPARP1:genotypeALC1KO            -0.5150     0.3999  -1.288   0.2053    
## Bleomycin:siRNAsiPARP1:genotypeALC1KO   0.6062     0.3824   1.585   0.1208    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.404 on 40 degrees of freedom
## Multiple R-squared:  0.8402, Adjusted R-squared:  0.8123 
## F-statistic: 30.05 on 7 and 40 DF,  p-value: 5.166e-14
cat("AIC: ", AIC(fit2))
## AIC:  58.46559
simres <- simulateResiduals(fittedModel = fit2)
plot(simres)

fit3 <- lm(NormCounts2 ~ Bleomycin*siRNA*genotype, data = dataset)
print(summary(fit3))
## 
## Call:
## lm(formula = NormCounts2 ~ Bleomycin * siRNA * genotype, data = dataset)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.2685 -0.1196  0.0231  0.1207  0.2482 
## 
## Coefficients:
##                                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                            0.99092    0.07553  13.120 4.47e-16 ***
## Bleomycin                             -0.52225    0.07222  -7.232 8.94e-09 ***
## siRNAsiPARP1                          -0.14330    0.10681  -1.342    0.187    
## genotypeALC1KO                        -0.14489    0.10681  -1.356    0.183    
## Bleomycin:siRNAsiPARP1                -0.03162    0.10213  -0.310    0.759    
## Bleomycin:genotypeALC1KO              -0.04619    0.10213  -0.452    0.654    
## siRNAsiPARP1:genotypeALC1KO            0.16411    0.15106   1.086    0.284    
## Bleomycin:siRNAsiPARP1:genotypeALC1KO  0.03281    0.14444   0.227    0.821    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1526 on 40 degrees of freedom
## Multiple R-squared:  0.8603, Adjusted R-squared:  0.8359 
## F-statistic: 35.19 on 7 and 40 DF,  p-value: 3.715e-15
cat("AIC: ", AIC(fit3))
## AIC:  -34.99918
simres <- simulateResiduals(fittedModel = fit3)
plot(simres)

fit4 <- lmer(Counts ~ Bleomycin*siRNA*genotype + (1|UID), data = dataset)
print(summary(fit4))
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Counts ~ Bleomycin * siRNA * genotype + (1 | UID)
##    Data: dataset
## 
## REML criterion at convergence: 634.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.7888 -0.4565 -0.1777  0.6047  2.2043 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  UID      (Intercept) 170269   412.6   
##  Residual             232885   482.6   
## Number of obs: 48, groups:  UID, 12
## 
## Fixed effects:
##                                       Estimate Std. Error      df t value
## (Intercept)                            1365.29     337.34   16.83   4.047
## Bleomycin                              -710.09     228.36   32.00  -3.109
## siRNAsiPARP1                           -397.90     477.07   16.83  -0.834
## genotypeALC1KO                          494.49     477.07   16.83   1.037
## Bleomycin:siRNAsiPARP1                   63.74     322.96   32.00   0.197
## Bleomycin:genotypeALC1KO               -518.54     322.96   32.00  -1.606
## siRNAsiPARP1:genotypeALC1KO            -243.48     674.67   16.83  -0.361
## Bleomycin:siRNAsiPARP1:genotypeALC1KO   376.67     456.73   32.00   0.825
##                                       Pr(>|t|)    
## (Intercept)                           0.000852 ***
## Bleomycin                             0.003920 ** 
## siRNAsiPARP1                          0.415934    
## genotypeALC1KO                        0.314618    
## Bleomycin:siRNAsiPARP1                0.844789    
## Bleomycin:genotypeALC1KO              0.118183    
## siRNAsiPARP1:genotypeALC1KO           0.722679    
## Bleomycin:siRNAsiPARP1:genotypeALC1KO 0.415639    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Blmycn sRNAsPARP1 gALC1K Bl:RNAPARP1 B:ALC1 sRNAPARP1:
## Bleomycin   -0.575                                                       
## siRNAsPARP1 -0.707  0.407                                                
## gntypALC1KO -0.707  0.407  0.500                                         
## Bl:RNAPARP1  0.407 -0.707 -0.575     -0.288                              
## Blmy:ALC1KO  0.407 -0.707 -0.288     -0.575  0.500                       
## sRNAPARP1:A  0.500 -0.288 -0.707     -0.707  0.407       0.407           
## B:RNAPARP1: -0.288  0.500  0.407      0.407 -0.707      -0.707 -0.575
cat("AIC: ", AIC(fit4))
## AIC:  654.7104
simres <- simulateResiduals(fittedModel = fit4)
plot(simres)

Quadratic formula

fit5 <- lm(Counts ~ Experiment + poly(Bleomycin,2)*siRNA*genotype, data = dataset)
print(summary(fit5))
## 
## Call:
## lm(formula = Counts ~ Experiment + poly(Bleomycin, 2) * siRNA * 
##     genotype, data = dataset)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1103.68  -248.93   -34.77   204.19   945.57 
## 
## Coefficients:
##                                                 Estimate Std. Error t value
## (Intercept)                                        262.6      160.7   1.634
## Experimentexp2                                     660.8      160.7   4.112
## Experimentexp3                                     837.8      160.7   5.213
## poly(Bleomycin, 2)1                              -3001.1      909.1  -3.301
## poly(Bleomycin, 2)2                                180.7      909.1   0.199
## siRNAsiPARP1                                      -343.8      185.6  -1.852
## genotypeALC1KO                                      54.0      185.6   0.291
## poly(Bleomycin, 2)1:siRNAsiPARP1                   269.4     1285.6   0.210
## poly(Bleomycin, 2)2:siRNAsiPARP1                  1012.1     1285.6   0.787
## poly(Bleomycin, 2)1:genotypeALC1KO               -2191.6     1285.6  -1.705
## poly(Bleomycin, 2)2:genotypeALC1KO                1616.6     1285.6   1.257
## siRNAsiPARP1:genotypeALC1KO                         76.5      262.4   0.292
## poly(Bleomycin, 2)1:siRNAsiPARP1:genotypeALC1KO   1592.0     1818.1   0.876
## poly(Bleomycin, 2)2:siRNAsiPARP1:genotypeALC1KO  -1661.6     1818.1  -0.914
##                                                 Pr(>|t|)    
## (Intercept)                                     0.111515    
## Experimentexp2                                  0.000235 ***
## Experimentexp3                                  9.09e-06 ***
## poly(Bleomycin, 2)1                             0.002268 ** 
## poly(Bleomycin, 2)2                             0.843612    
## siRNAsiPARP1                                    0.072653 .  
## genotypeALC1KO                                  0.772812    
## poly(Bleomycin, 2)1:siRNAsiPARP1                0.835271    
## poly(Bleomycin, 2)2:siRNAsiPARP1                0.436588    
## poly(Bleomycin, 2)1:genotypeALC1KO              0.097371 .  
## poly(Bleomycin, 2)2:genotypeALC1KO              0.217160    
## siRNAsiPARP1:genotypeALC1KO                     0.772429    
## poly(Bleomycin, 2)1:siRNAsiPARP1:genotypeALC1KO 0.387384    
## poly(Bleomycin, 2)2:siRNAsiPARP1:genotypeALC1KO 0.367195    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 454.5 on 34 degrees of freedom
## Multiple R-squared:  0.763,  Adjusted R-squared:  0.6724 
## F-statistic: 8.419 on 13 and 34 DF,  p-value: 3.064e-07
cat("AIC: ", AIC(fit5))
## AIC:  737.115
simres <- simulateResiduals(fittedModel = fit5)
plot(simres)

fit6 <- lm(NormCounts ~ poly(Bleomycin,2)*siRNA*genotype, data = dataset)
print(summary(fit6))
## 
## Call:
## lm(formula = NormCounts ~ poly(Bleomycin, 2) * siRNA * genotype, 
##     data = dataset)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.42105 -0.11261 -0.00354  0.08330  0.54949 
## 
## Coefficients:
##                                                   Estimate Std. Error t value
## (Intercept)                                      1.000e+00  5.461e-02  18.313
## poly(Bleomycin, 2)1                             -4.033e+00  3.783e-01 -10.660
## poly(Bleomycin, 2)2                              2.759e-01  3.783e-01   0.729
## siRNAsiPARP1                                    -1.132e-16  7.723e-02   0.000
## genotypeALC1KO                                  -3.754e-16  7.723e-02   0.000
## poly(Bleomycin, 2)1:siRNAsiPARP1                -2.174e+00  5.350e-01  -4.063
## poly(Bleomycin, 2)2:siRNAsiPARP1                 2.333e+00  5.350e-01   4.360
## poly(Bleomycin, 2)1:genotypeALC1KO              -2.582e+00  5.350e-01  -4.827
## poly(Bleomycin, 2)2:genotypeALC1KO               2.655e+00  5.350e-01   4.961
## siRNAsiPARP1:genotypeALC1KO                      2.671e-16  1.092e-01   0.000
## poly(Bleomycin, 2)1:siRNAsiPARP1:genotypeALC1KO  2.562e+00  7.567e-01   3.386
## poly(Bleomycin, 2)2:siRNAsiPARP1:genotypeALC1KO -2.919e+00  7.567e-01  -3.857
##                                                 Pr(>|t|)    
## (Intercept)                                      < 2e-16 ***
## poly(Bleomycin, 2)1                             1.09e-12 ***
## poly(Bleomycin, 2)2                             0.470521    
## siRNAsiPARP1                                    1.000000    
## genotypeALC1KO                                  1.000000    
## poly(Bleomycin, 2)1:siRNAsiPARP1                0.000251 ***
## poly(Bleomycin, 2)2:siRNAsiPARP1                0.000104 ***
## poly(Bleomycin, 2)1:genotypeALC1KO              2.55e-05 ***
## poly(Bleomycin, 2)2:genotypeALC1KO              1.69e-05 ***
## siRNAsiPARP1:genotypeALC1KO                     1.000000    
## poly(Bleomycin, 2)1:siRNAsiPARP1:genotypeALC1KO 0.001727 ** 
## poly(Bleomycin, 2)2:siRNAsiPARP1:genotypeALC1KO 0.000456 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1892 on 36 degrees of freedom
## Multiple R-squared:  0.9685, Adjusted R-squared:  0.9588 
## F-statistic: 100.5 on 11 and 36 DF,  p-value: < 2.2e-16
cat("AIC: ", AIC(fit6))
## AIC:  -11.44408
simres <- simulateResiduals(fittedModel = fit6)
plot(simres)

fit7 <- lm(NormCounts2 ~ poly(Bleomycin,2)*siRNA*genotype, data = dataset)
print(summary(fit7))
## 
## Call:
## lm(formula = NormCounts2 ~ poly(Bleomycin, 2) * siRNA * genotype, 
##     data = dataset)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.152905 -0.045876 -0.001149  0.038348  0.199550 
## 
## Coefficients:
##                                                 Estimate Std. Error t value
## (Intercept)                                      0.54728    0.02136  25.618
## poly(Bleomycin, 2)1                             -2.20725    0.14801 -14.913
## poly(Bleomycin, 2)2                              0.15101    0.14801   1.020
## siRNAsiPARP1                                    -0.17015    0.03021  -5.632
## genotypeALC1KO                                  -0.18412    0.03021  -6.094
## poly(Bleomycin, 2)1:siRNAsiPARP1                -0.13362    0.20931  -0.638
## poly(Bleomycin, 2)2:siRNAsiPARP1                 0.83278    0.20931   3.979
## poly(Bleomycin, 2)1:genotypeALC1KO              -0.19522    0.20931  -0.933
## poly(Bleomycin, 2)2:genotypeALC1KO               0.91321    0.20931   4.363
## siRNAsiPARP1:genotypeALC1KO                      0.19198    0.04273   4.493
## poly(Bleomycin, 2)1:siRNAsiPARP1:genotypeALC1KO  0.13865    0.29601   0.468
## poly(Bleomycin, 2)2:siRNAsiPARP1:genotypeALC1KO -0.99441    0.29601  -3.359
##                                                 Pr(>|t|)    
## (Intercept)                                      < 2e-16 ***
## poly(Bleomycin, 2)1                              < 2e-16 ***
## poly(Bleomycin, 2)2                             0.314405    
## siRNAsiPARP1                                    2.16e-06 ***
## genotypeALC1KO                                  5.20e-07 ***
## poly(Bleomycin, 2)1:siRNAsiPARP1                0.527273    
## poly(Bleomycin, 2)2:siRNAsiPARP1                0.000321 ***
## poly(Bleomycin, 2)1:genotypeALC1KO              0.357198    
## poly(Bleomycin, 2)2:genotypeALC1KO              0.000103 ***
## siRNAsiPARP1:genotypeALC1KO                     6.99e-05 ***
## poly(Bleomycin, 2)1:siRNAsiPARP1:genotypeALC1KO 0.642327    
## poly(Bleomycin, 2)2:siRNAsiPARP1:genotypeALC1KO 0.001859 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.074 on 36 degrees of freedom
## Multiple R-squared:  0.9704, Adjusted R-squared:  0.9614 
## F-statistic: 107.4 on 11 and 36 DF,  p-value: < 2.2e-16
cat("AIC: ", AIC(fit7))
## AIC:  -101.5405
simres <- simulateResiduals(fittedModel = fit7)
plot(simres)

fit8 <- lmer(Counts ~ poly(Bleomycin,2)*siRNA*genotype + (1|UID), data = dataset)
print(summary(fit8))
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Counts ~ poly(Bleomycin, 2) * siRNA * genotype + (1 | UID)
##    Data: dataset
## 
## REML criterion at convergence: 554.3
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.40400 -0.26632 -0.08172  0.47563  1.71356 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  UID      (Intercept) 175352   418.8   
##  Residual             212556   461.0   
## Number of obs: 48, groups:  UID, 12
## 
## Fixed effects:
##                                                 Estimate Std. Error      df
## (Intercept)                                        762.1      276.0     8.0
## poly(Bleomycin, 2)1                              -3001.1      922.1    28.0
## poly(Bleomycin, 2)2                                180.7      922.1    28.0
## siRNAsiPARP1                                      -343.8      390.3     8.0
## genotypeALC1KO                                      54.0      390.3     8.0
## poly(Bleomycin, 2)1:siRNAsiPARP1                   269.4     1304.0    28.0
## poly(Bleomycin, 2)2:siRNAsiPARP1                  1012.1     1304.0    28.0
## poly(Bleomycin, 2)1:genotypeALC1KO               -2191.6     1304.0    28.0
## poly(Bleomycin, 2)2:genotypeALC1KO                1616.6     1304.0    28.0
## siRNAsiPARP1:genotypeALC1KO                         76.5      552.0     8.0
## poly(Bleomycin, 2)1:siRNAsiPARP1:genotypeALC1KO   1592.0     1844.2    28.0
## poly(Bleomycin, 2)2:siRNAsiPARP1:genotypeALC1KO  -1661.6     1844.2    28.0
##                                                 t value Pr(>|t|)   
## (Intercept)                                       2.761  0.02462 * 
## poly(Bleomycin, 2)1                              -3.255  0.00296 **
## poly(Bleomycin, 2)2                               0.196  0.84604   
## siRNAsiPARP1                                     -0.881  0.40413   
## genotypeALC1KO                                    0.138  0.89338   
## poly(Bleomycin, 2)1:siRNAsiPARP1                  0.207  0.83783   
## poly(Bleomycin, 2)2:siRNAsiPARP1                  0.776  0.44417   
## poly(Bleomycin, 2)1:genotypeALC1KO               -1.681  0.10396   
## poly(Bleomycin, 2)2:genotypeALC1KO                1.240  0.22538   
## siRNAsiPARP1:genotypeALC1KO                       0.139  0.89319   
## poly(Bleomycin, 2)1:siRNAsiPARP1:genotypeALC1KO   0.863  0.39533   
## poly(Bleomycin, 2)2:siRNAsiPARP1:genotypeALC1KO  -0.901  0.37526   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##                   (Intr) pl(B,2)1 pl(B,2)2 sRNAsPARP1 gALC1K pl(B,2)1:RNAPARP1
## ply(Blm,2)1        0.000                                                      
## ply(Blm,2)2        0.000  0.000                                               
## siRNAsPARP1       -0.707  0.000    0.000                                      
## gntypALC1KO       -0.707  0.000    0.000    0.500                             
## pl(B,2)1:RNAPARP1  0.000 -0.707    0.000    0.000      0.000                  
## pl(B,2)2:RNAPARP1  0.000  0.000   -0.707    0.000      0.000  0.000           
## p(B,2)1:ALC        0.000 -0.707    0.000    0.000      0.000  0.500           
## p(B,2)2:ALC        0.000  0.000   -0.707    0.000      0.000  0.000           
## sRNAPARP1:A        0.500  0.000    0.000   -0.707     -0.707  0.000           
## p(B,2)1:RNAPARP1:  0.000  0.500    0.000    0.000      0.000 -0.707           
## p(B,2)2:RNAPARP1:  0.000  0.000    0.500    0.000      0.000  0.000           
##                   pl(B,2)2:RNAPARP1 p(B,2)1:A p(B,2)2:A sRNAPARP1:
## ply(Blm,2)1                                                       
## ply(Blm,2)2                                                       
## siRNAsPARP1                                                       
## gntypALC1KO                                                       
## pl(B,2)1:RNAPARP1                                                 
## pl(B,2)2:RNAPARP1                                                 
## p(B,2)1:ALC        0.000                                          
## p(B,2)2:ALC        0.500             0.000                        
## sRNAPARP1:A        0.000             0.000     0.000              
## p(B,2)1:RNAPARP1:  0.000            -0.707     0.000     0.000    
## p(B,2)2:RNAPARP1: -0.707             0.000    -0.707     0.000    
##                   p(B,2)1:RNAPARP1:
## ply(Blm,2)1                        
## ply(Blm,2)2                        
## siRNAsPARP1                        
## gntypALC1KO                        
## pl(B,2)1:RNAPARP1                  
## pl(B,2)2:RNAPARP1                  
## p(B,2)1:ALC                        
## p(B,2)2:ALC                        
## sRNAPARP1:A                        
## p(B,2)1:RNAPARP1:                  
## p(B,2)2:RNAPARP1:  0.000
cat("AIC: ", AIC(fit8))
## AIC:  582.2921
simres <- simulateResiduals(fittedModel = fit8)
plot(simres)

Cubic formula

fit9 <- lm(Counts ~ Experiment + poly(Bleomycin,3)*siRNA*genotype, data = dataset)
print(summary(fit9))
## 
## Call:
## lm(formula = Counts ~ Experiment + poly(Bleomycin, 3) * siRNA * 
##     genotype, data = dataset)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1085.48  -228.62   -45.29   223.66   963.77 
## 
## Coefficients:
##                                                 Estimate Std. Error t value
## (Intercept)                                       262.56     169.01   1.554
## Experimentexp2                                    660.81     169.01   3.910
## Experimentexp3                                    837.75     169.01   4.957
## poly(Bleomycin, 3)1                             -3001.14     956.05  -3.139
## poly(Bleomycin, 3)2                               180.71     956.05   0.189
## poly(Bleomycin, 3)3                               562.81     956.05   0.589
## siRNAsiPARP1                                     -343.75     195.15  -1.761
## genotypeALC1KO                                     54.00     195.15   0.277
## poly(Bleomycin, 3)1:siRNAsiPARP1                  269.40    1352.06   0.199
## poly(Bleomycin, 3)2:siRNAsiPARP1                 1012.10    1352.06   0.749
## poly(Bleomycin, 3)3:siRNAsiPARP1                 -865.23    1352.06  -0.640
## poly(Bleomycin, 3)1:genotypeALC1KO              -2191.58    1352.06  -1.621
## poly(Bleomycin, 3)2:genotypeALC1KO               1616.58    1352.06   1.196
## poly(Bleomycin, 3)3:genotypeALC1KO                -51.65    1352.06  -0.038
## siRNAsiPARP1:genotypeALC1KO                        76.50     275.99   0.277
## poly(Bleomycin, 3)1:siRNAsiPARP1:genotypeALC1KO  1591.97    1912.10   0.833
## poly(Bleomycin, 3)2:siRNAsiPARP1:genotypeALC1KO -1661.61    1912.10  -0.869
## poly(Bleomycin, 3)3:siRNAsiPARP1:genotypeALC1KO   272.12    1912.10   0.142
##                                                 Pr(>|t|)    
## (Intercept)                                     0.130778    
## Experimentexp2                                  0.000489 ***
## Experimentexp3                                  2.63e-05 ***
## poly(Bleomycin, 3)1                             0.003787 ** 
## poly(Bleomycin, 3)2                             0.851352    
## poly(Bleomycin, 3)3                             0.560482    
## siRNAsiPARP1                                    0.088354 .  
## genotypeALC1KO                                  0.783903    
## poly(Bleomycin, 3)1:siRNAsiPARP1                0.843413    
## poly(Bleomycin, 3)2:siRNAsiPARP1                0.459953    
## poly(Bleomycin, 3)3:siRNAsiPARP1                0.527071    
## poly(Bleomycin, 3)1:genotypeALC1KO              0.115500    
## poly(Bleomycin, 3)2:genotypeALC1KO              0.241202    
## poly(Bleomycin, 3)3:genotypeALC1KO              0.969779    
## siRNAsiPARP1:genotypeALC1KO                     0.783538    
## poly(Bleomycin, 3)1:siRNAsiPARP1:genotypeALC1KO 0.411663    
## poly(Bleomycin, 3)2:siRNAsiPARP1:genotypeALC1KO 0.391747    
## poly(Bleomycin, 3)3:siRNAsiPARP1:genotypeALC1KO 0.887783    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 478 on 30 degrees of freedom
## Multiple R-squared:  0.7687, Adjusted R-squared:  0.6376 
## F-statistic: 5.864 on 17 and 30 DF,  p-value: 1.367e-05
cat("AIC: ", AIC(fit9))
## AIC:  743.9456
simres <- simulateResiduals(fittedModel = fit9)
plot(simres)

fit10 <- lm(NormCounts ~ poly(Bleomycin,3)*siRNA*genotype, data = dataset)
print(summary(fit10))
## 
## Call:
## lm(formula = NormCounts ~ poly(Bleomycin, 3) * siRNA * genotype, 
##     data = dataset)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.42204 -0.04856 -0.01251  0.06747  0.55506 
## 
## Coefficients:
##                                                   Estimate Std. Error t value
## (Intercept)                                      1.000e+00  5.157e-02  19.391
## poly(Bleomycin, 3)1                             -4.033e+00  3.573e-01 -11.288
## poly(Bleomycin, 3)2                              2.759e-01  3.573e-01   0.772
## poly(Bleomycin, 3)3                              6.525e-01  3.573e-01   1.826
## siRNAsiPARP1                                    -1.984e-16  7.293e-02   0.000
## genotypeALC1KO                                  -4.092e-16  7.293e-02   0.000
## poly(Bleomycin, 3)1:siRNAsiPARP1                -2.174e+00  5.053e-01  -4.303
## poly(Bleomycin, 3)2:siRNAsiPARP1                 2.333e+00  5.053e-01   4.617
## poly(Bleomycin, 3)3:siRNAsiPARP1                -1.425e+00  5.053e-01  -2.821
## poly(Bleomycin, 3)1:genotypeALC1KO              -2.582e+00  5.053e-01  -5.111
## poly(Bleomycin, 3)2:genotypeALC1KO               2.655e+00  5.053e-01   5.254
## poly(Bleomycin, 3)3:genotypeALC1KO              -6.802e-01  5.053e-01  -1.346
## siRNAsiPARP1:genotypeALC1KO                      2.417e-16  1.031e-01   0.000
## poly(Bleomycin, 3)1:siRNAsiPARP1:genotypeALC1KO  2.562e+00  7.146e-01   3.585
## poly(Bleomycin, 3)2:siRNAsiPARP1:genotypeALC1KO -2.919e+00  7.146e-01  -4.084
## poly(Bleomycin, 3)3:siRNAsiPARP1:genotypeALC1KO  1.243e+00  7.146e-01   1.740
##                                                 Pr(>|t|)    
## (Intercept)                                      < 2e-16 ***
## poly(Bleomycin, 3)1                             1.08e-12 ***
## poly(Bleomycin, 3)2                             0.445621    
## poly(Bleomycin, 3)3                             0.077154 .  
## siRNAsiPARP1                                    1.000000    
## genotypeALC1KO                                  1.000000    
## poly(Bleomycin, 3)1:siRNAsiPARP1                0.000149 ***
## poly(Bleomycin, 3)2:siRNAsiPARP1                6.03e-05 ***
## poly(Bleomycin, 3)3:siRNAsiPARP1                0.008160 ** 
## poly(Bleomycin, 3)1:genotypeALC1KO              1.44e-05 ***
## poly(Bleomycin, 3)2:genotypeALC1KO              9.50e-06 ***
## poly(Bleomycin, 3)3:genotypeALC1KO              0.187700    
## siRNAsiPARP1:genotypeALC1KO                     1.000000    
## poly(Bleomycin, 3)1:siRNAsiPARP1:genotypeALC1KO 0.001104 ** 
## poly(Bleomycin, 3)2:siRNAsiPARP1:genotypeALC1KO 0.000276 ***
## poly(Bleomycin, 3)3:siRNAsiPARP1:genotypeALC1KO 0.091497 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1786 on 32 degrees of freedom
## Multiple R-squared:  0.975,  Adjusted R-squared:  0.9633 
## F-statistic: 83.23 on 15 and 32 DF,  p-value: < 2.2e-16
cat("AIC: ", AIC(fit10))
## AIC:  -14.59004
simres <- simulateResiduals(fittedModel = fit10)
plot(simres)

fit11 <- lm(NormCounts2 ~ poly(Bleomycin,3)*siRNA*genotype, data = dataset)
print(summary(fit11))
## 
## Call:
## lm(formula = NormCounts2 ~ poly(Bleomycin, 3) * siRNA * genotype, 
##     data = dataset)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.15326 -0.01990 -0.00463  0.03012  0.20157 
## 
## Coefficients:
##                                                 Estimate Std. Error t value
## (Intercept)                                      0.54728    0.01926  28.421
## poly(Bleomycin, 3)1                             -2.20725    0.13341 -16.545
## poly(Bleomycin, 3)2                              0.15101    0.13341   1.132
## poly(Bleomycin, 3)3                              0.35710    0.13341   2.677
## siRNAsiPARP1                                    -0.17015    0.02723  -6.248
## genotypeALC1KO                                  -0.18412    0.02723  -6.761
## poly(Bleomycin, 3)1:siRNAsiPARP1                -0.13362    0.18867  -0.708
## poly(Bleomycin, 3)2:siRNAsiPARP1                 0.83278    0.18867   4.414
## poly(Bleomycin, 3)3:siRNAsiPARP1                -0.64854    0.18867  -3.437
## poly(Bleomycin, 3)1:genotypeALC1KO              -0.19522    0.18867  -1.035
## poly(Bleomycin, 3)2:genotypeALC1KO               0.91321    0.18867   4.840
## poly(Bleomycin, 3)3:genotypeALC1KO              -0.36716    0.18867  -1.946
## siRNAsiPARP1:genotypeALC1KO                      0.19198    0.03851   4.985
## poly(Bleomycin, 3)1:siRNAsiPARP1:genotypeALC1KO  0.13865    0.26682   0.520
## poly(Bleomycin, 3)2:siRNAsiPARP1:genotypeALC1KO -0.99441    0.26682  -3.727
## poly(Bleomycin, 3)3:siRNAsiPARP1:genotypeALC1KO  0.57786    0.26682   2.166
##                                                 Pr(>|t|)    
## (Intercept)                                      < 2e-16 ***
## poly(Bleomycin, 3)1                              < 2e-16 ***
## poly(Bleomycin, 3)2                             0.266081    
## poly(Bleomycin, 3)3                             0.011626 *  
## siRNAsiPARP1                                    5.30e-07 ***
## genotypeALC1KO                                  1.22e-07 ***
## poly(Bleomycin, 3)1:siRNAsiPARP1                0.483934    
## poly(Bleomycin, 3)2:siRNAsiPARP1                0.000108 ***
## poly(Bleomycin, 3)3:siRNAsiPARP1                0.001648 ** 
## poly(Bleomycin, 3)1:genotypeALC1KO              0.308549    
## poly(Bleomycin, 3)2:genotypeALC1KO              3.16e-05 ***
## poly(Bleomycin, 3)3:genotypeALC1KO              0.060472 .  
## siRNAsiPARP1:genotypeALC1KO                     2.08e-05 ***
## poly(Bleomycin, 3)1:siRNAsiPARP1:genotypeALC1KO 0.606887    
## poly(Bleomycin, 3)2:siRNAsiPARP1:genotypeALC1KO 0.000749 ***
## poly(Bleomycin, 3)3:siRNAsiPARP1:genotypeALC1KO 0.037891 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.0667 on 32 degrees of freedom
## Multiple R-squared:  0.9787, Adjusted R-squared:  0.9686 
## F-statistic: 97.79 on 15 and 32 DF,  p-value: < 2.2e-16
cat("AIC: ", AIC(fit11))
## AIC:  -109.1627
simres <- simulateResiduals(fittedModel = fit11)
plot(simres)

fit12 <- lmer(Counts ~ poly(Bleomycin,3)*siRNA*genotype + (1|UID), data = dataset)
print(summary(fit12))
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Counts ~ poly(Bleomycin, 3) * siRNA * genotype + (1 | UID)
##    Data: dataset
## 
## REML criterion at convergence: 491.3
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.26168 -0.23317  0.02957  0.46884  1.68726 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  UID      (Intercept) 168256   410.2   
##  Residual             240938   490.9   
## Number of obs: 48, groups:  UID, 12
## 
## Fixed effects:
##                                                 Estimate Std. Error       df
## (Intercept)                                       762.08     275.98     8.00
## poly(Bleomycin, 3)1                             -3001.14     981.71    24.00
## poly(Bleomycin, 3)2                               180.71     981.71    24.00
## poly(Bleomycin, 3)3                               562.81     981.71    24.00
## siRNAsiPARP1                                     -343.75     390.29     8.00
## genotypeALC1KO                                     54.00     390.29     8.00
## poly(Bleomycin, 3)1:siRNAsiPARP1                  269.39    1388.35    24.00
## poly(Bleomycin, 3)2:siRNAsiPARP1                 1012.10    1388.35    24.00
## poly(Bleomycin, 3)3:siRNAsiPARP1                 -865.23    1388.35    24.00
## poly(Bleomycin, 3)1:genotypeALC1KO              -2191.58    1388.35    24.00
## poly(Bleomycin, 3)2:genotypeALC1KO               1616.58    1388.35    24.00
## poly(Bleomycin, 3)3:genotypeALC1KO                -51.65    1388.35    24.00
## siRNAsiPARP1:genotypeALC1KO                        76.50     551.96     8.00
## poly(Bleomycin, 3)1:siRNAsiPARP1:genotypeALC1KO  1591.97    1963.42    24.00
## poly(Bleomycin, 3)2:siRNAsiPARP1:genotypeALC1KO -1661.61    1963.42    24.00
## poly(Bleomycin, 3)3:siRNAsiPARP1:genotypeALC1KO   272.12    1963.42    24.00
##                                                 t value Pr(>|t|)   
## (Intercept)                                       2.761  0.02462 * 
## poly(Bleomycin, 3)1                              -3.057  0.00542 **
## poly(Bleomycin, 3)2                               0.184  0.85550   
## poly(Bleomycin, 3)3                               0.573  0.57178   
## siRNAsiPARP1                                     -0.881  0.40413   
## genotypeALC1KO                                    0.138  0.89338   
## poly(Bleomycin, 3)1:siRNAsiPARP1                  0.194  0.84778   
## poly(Bleomycin, 3)2:siRNAsiPARP1                  0.729  0.47306   
## poly(Bleomycin, 3)3:siRNAsiPARP1                 -0.623  0.53902   
## poly(Bleomycin, 3)1:genotypeALC1KO               -1.579  0.12753   
## poly(Bleomycin, 3)2:genotypeALC1KO                1.164  0.25571   
## poly(Bleomycin, 3)3:genotypeALC1KO               -0.037  0.97063   
## siRNAsiPARP1:genotypeALC1KO                       0.139  0.89319   
## poly(Bleomycin, 3)1:siRNAsiPARP1:genotypeALC1KO   0.811  0.42544   
## poly(Bleomycin, 3)2:siRNAsiPARP1:genotypeALC1KO  -0.846  0.40575   
## poly(Bleomycin, 3)3:siRNAsiPARP1:genotypeALC1KO   0.139  0.89093   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
cat("AIC: ", AIC(fit12))
## AIC:  527.3356
simres <- simulateResiduals(fittedModel = fit12)
plot(simres)

Compare Results

ICtab(fit1,fit2,fit3,fit4,
      fit5,fit6,fit7,fit8,
      fit9,fit10,fit11,fit12,
      base=T)
##       AIC    dAIC   df
## fit11 -109.2    0.0 17
## fit7  -101.5    7.6 13
## fit3   -35.0   74.2 9 
## fit10  -14.6   94.6 17
## fit6   -11.4   97.7 13
## fit2    58.5  167.6 9 
## fit12  527.3  636.5 18
## fit8   582.3  691.5 14
## fit4   654.7  763.9 10
## fit5   737.1  846.3 15
## fit1   738.4  847.6 11
## fit9   743.9  853.1 19

Final Result

fit <- fit7

output <- coef(summary(fit))
output <- output[grep("Bleomycin", rownames(output)),]


rownames(output) <- gsub("poly\\(|, [1-3]\\)","", rownames(output) )
rownames(output) <- gsub("genotype",  paste0(" ",levels(dataset$genotype)[1], " vs. "), rownames(output))
rownames(output)[!(grepl("vs", rownames(output)))] <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$genotype)[1],  sep = " in " )

rownames(output) <- gsub("siRNA",  paste0(" ",levels(dataset$siRNA)[1], " vs. "), rownames(output))
rownames(output)[!(grepl("vs.*vs| in ", rownames(output)))] <- paste(rownames(output)[!(grepl("vs.*vs| in ", rownames(output)))], levels(dataset$siRNA)[1],  sep = " in " )

rownames(output)[!(grepl("vs", rownames(output)))] <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$siRNA)[1],  sep = " " )


# suggested result table
kable(output, row.names = T)
Estimate Std. Error t value Pr(>|t|)
Bleomycin1 in WT siCtrl -2.2072495 0.1480067 -14.9131713 0.0000000
Bleomycin2 in WT siCtrl 0.1510077 0.1480067 1.0202760 0.3144054
Bleomycin1: siCtrl vs. siPARP1 in WT -0.1336194 0.2093131 -0.6383711 0.5272726
Bleomycin2: siCtrl vs. siPARP1 in WT 0.8327816 0.2093131 3.9786406 0.0003209
Bleomycin1: WT vs. ALC1KO in siCtrl -0.1952223 0.2093131 -0.9326808 0.3571983
Bleomycin2: WT vs. ALC1KO in siCtrl 0.9132115 0.2093131 4.3628969 0.0001032
Bleomycin1: siCtrl vs. siPARP1: WT vs. ALC1KO 0.1386508 0.2960134 0.4683935 0.6423270
Bleomycin2: siCtrl vs. siPARP1: WT vs. ALC1KO -0.9944078 0.2960134 -3.3593333 0.0018587
write.table(output, file = "FigureS3_bleo_Stats_Ref_WT.txt", quote = F, sep = "\t", row.names = T, col.names = NA)
# re-fit with ALC1KO reference
dataset$genotype <- relevel(dataset$genotype, ref = "ALC1KO")

fit <- lm(NormCounts2 ~ poly(Bleomycin,2)*siRNA*genotype, data = dataset)

output <- coef(summary(fit))
output <- output[grep("Bleomycin", rownames(output)),]


rownames(output) <- gsub("poly\\(|, [1-3]\\)","", rownames(output) )
rownames(output) <- gsub("genotype",  paste0(" ",levels(dataset$genotype)[1], " vs. "), rownames(output))
rownames(output)[!(grepl("vs", rownames(output)))] <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$genotype)[1],  sep = " in " )

rownames(output) <- gsub("siRNA",  paste0(" ",levels(dataset$siRNA)[1], " vs. "), rownames(output))
rownames(output)[!(grepl("vs.*vs| in ", rownames(output)))] <- paste(rownames(output)[!(grepl("vs.*vs| in ", rownames(output)))], levels(dataset$siRNA)[1],  sep = " in " )

rownames(output)[!(grepl("vs", rownames(output)))] <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$siRNA)[1],  sep = " " )


# suggested result table
kable(output, row.names = T)
Estimate Std. Error t value Pr(>|t|)
Bleomycin1 in ALC1KO siCtrl -2.4024719 0.1480067 -16.2321812 0.0000000
Bleomycin2 in ALC1KO siCtrl 1.0642192 0.1480067 7.1903440 0.0000000
Bleomycin1: siCtrl vs. siPARP1 in ALC1KO 0.0050313 0.2093131 0.0240374 0.9809555
Bleomycin2: siCtrl vs. siPARP1 in ALC1KO -0.1616262 0.2093131 -0.7721741 0.4450512
Bleomycin1: ALC1KO vs. WT in siCtrl 0.1952223 0.2093131 0.9326808 0.3571983
Bleomycin2: ALC1KO vs. WT in siCtrl -0.9132115 0.2093131 -4.3628969 0.0001032
Bleomycin1: siCtrl vs. siPARP1: ALC1KO vs. WT -0.1386508 0.2960134 -0.4683935 0.6423270
Bleomycin2: siCtrl vs. siPARP1: ALC1KO vs. WT 0.9944078 0.2960134 3.3593333 0.0018587
write.table(output, file = "FigureS3_bleo_Stats_Ref_ALC1KO.txt", quote = F, sep = "\t", row.names = T, col.names = NA)

Anova

fit7a <- lm(NormCounts2 ~ poly(Bleomycin,2)*siRNA*genotype, data = dataset)
fit7b <- lm(NormCounts2 ~ poly(Bleomycin,2)*siRNA+genotype, data = dataset)

# anova table
anova(fit7a, fit7b)
## Analysis of Variance Table
## 
## Model 1: NormCounts2 ~ poly(Bleomycin, 2) * siRNA * genotype
## Model 2: NormCounts2 ~ poly(Bleomycin, 2) * siRNA + genotype
##   Res.Df     RSS Df Sum of Sq      F    Pr(>F)    
## 1     36 0.19715                                  
## 2     41 0.41795 -5   -0.2208 8.0635 3.497e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
fit7c <- lm(NormCounts2 ~ poly(Bleomycin,2)*genotype*siRNA, data = dataset)
fit7d <- lm(NormCounts2 ~ poly(Bleomycin,2)*genotype+siRNA, data = dataset)

# anova table
anova(fit7c, fit7d)
## Analysis of Variance Table
## 
## Model 1: NormCounts2 ~ poly(Bleomycin, 2) * genotype * siRNA
## Model 2: NormCounts2 ~ poly(Bleomycin, 2) * genotype + siRNA
##   Res.Df     RSS Df Sum of Sq      F    Pr(>F)    
## 1     36 0.19715                                  
## 2     41 0.39991 -5  -0.20276 7.4046 7.319e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1